关键词： slicing resource allocation algorithm   radio access network model   mobile edge computing   network system   service requirements   telecommunication network reliability   6G mobile communication   resource allocation   edge computing   optimisation   energy consumption   telecommunication scheduling   enhanced mobile broadband slicing users   stability-oriented RAN slicing   short time interval resource allocation algorithms   computing power   computation offloading   radio access networks   joint communication   slicing resources   ultra-reliable low latency communication slicing users   mobile computing   5G mobile communication   stable allocation   base stations   URLLC slices   telecommunication computing  

摘要： Network slicing and mobile edge computing are key technologies in 5G/6G networks. Stable allocation of slicing resources is a challenge while communication and computation offloading coexist. To meet the service requirements of communication and computation offloading, a radio access network model which consists of enhanced mobile broadband (eMBB) slicing users, ultra-reliable low latency communication (URLLC) slicing users, base stations integrated with MEC servers, and fog nodes with computing power are proposed. By employing the Lyapunov optimisation technique, a slicing resource allocation algorithm is proposed to maintain the queue stability and minimise the energy consumption of the system. For eMBB and URLLC slices, long time interval and short time interval resource allocation algorithms are proposed respectively. Simulation results show that the proposed algorithm can guarantee the long-term stability of the network system while ensuring the service requirements.

关键词： Servers   Power demand   Cloud computing   Mobile handsets   Task analysis   Computer architecture   Load management   5G networks   mobile edge computing   hotspot mitigation   energy saving  

摘要： Due to the long latency of accessing services from cloud datacenters, mobile edge computing (MEC) has been proposed to provide the capability of cloud computing in close proximity to mobile users. Mobile devices can offload computation-intensive applications to MEC servers to achieve better performance and reduce power consumption. Owing to user mobility, workloads of MEC servers would vary severely to cause imbalanced loads and incur hotspot servers. Previous studies of power saving and hotspot management apply mechanisms for cloud datacenters to MEC servers, but these studies may not be suitable for MEC servers due to the limited computation and communication capacity. We are motivated to propose a scheme, complementary offloading for hotspot mitigation (COHM), for MEC. COHM consists of mechanisms for MEC servers with different states, namely busy, idle, and available. These mechanisms jointly consider the computation tasks for MEC servers with different states to mitigate hotspots. The simulation results show that COHM can decrease the number of hotspot servers without degrading the quality of service for applications.

关键词： Mobile edge computing   Task offloading   Latency   QoS   VM migration  

摘要： Due to the rise of mobile users and their demand for various applications, the traditional cloud computing remote servers consume huge energy and execution time because it is far away from the mobile devices. The Mobile edge computing is a technology which offloads the workloads of mobile user device to the edge of the network using Mobile Edge Computing thereby reducing the burden of cloud and reducing the latency and energy consumption. However, offloading of the complex tasks face challenges like increased delay and energy consumption. To solve these challenges, proposed approach designs a Delay-Energy aware Task Offloading Algorithm (DETOA) where the tasks gets partitioned based on the delay, energy and communication constraints. The proposed task partitioning based approach executes the tasks locally, if the MUD can process the task else the tasks are offloaded to MEC and processed remotely. In addition, a VM migration scheme is introduced to prevent service degradation through task and VM replication approach. The proposed approach exhibited better performance in terms of energy, delay and QoS.

关键词： Eavesdropping   Task analysis   Jamming   Computational modeling   Surveillance   Receivers   Mobile handsets   Legitimate surveillance   proactive eavesdropping   mobile edge computing   computation offloading   suspicious communications  

摘要： In this paper, the legitimate surveillance of a suspicious mobile edge computing (MEC) network consisting of a suspicious edge server (SES) and multiple suspicious users (SUs), in the presence of a full-duplex monitor is studied. Each SU has a computation task to complete within a time deadline and can completely or partially offload the task to SES, while the monitor can either jam or assist the suspicious communications during task uploading and result downloading. With the heterogeneous offloading model adopted by the SUs, the problem of optimizing the monitor mode and transmit power to maximize the average ratio of successfully eavesdropped tasks, subject to the monitor transmit power constraint and the task completion time deadline constraint is investigated. The problem is solved via exploring the particular problem structure and adopting the sum-of-ratios optimization. Simulation results show that the proposed algorithm significantly outperforms the benchmark algorithms, especially for the SUs with partial offloading. It is also shown that the proposed algorithm is of low-complexity and achieves almost the same performance as the high-complexity optimal algorithm. Besides, compared to the SUs with binary offloading, the eavesdropping performance for the SUs with partial offloading is shown to be much better.

关键词： Reliability   Wireless communication   Videos   Edge computing   Delays   Bandwidth   Task analysis   Virtual reality   sub-6 GHz and mmWave heterogeneous networks   reliability enhancement   mobile edge computing   stochastic geometry  

摘要： The reliability of current virtual reality (VR) delivery is low due to the limited resources on VR head-mounted displays (HMDs) and the transmission rate bottleneck of sub-6 GHz networks. In this paper, we propose a dual-connectivity sub-6 GHz and mmWave heterogeneous network architecture empowered by mobile edge capability. The core idea of the proposed architecture is to utilize the complementary advantages of sub-6 GHz links and mmWave links to conduct a collaborative edge resource design, which aims to improve the reliability of VR delivery. From the perspective of stochastic geometry, we analyze the reliability of VR delivery and theoretically demonstrate that sub-6 GHz links can be used to enhance the reliability of VR delivery despite the large mmWave bandwidth. Based on our analytical work, we formulate a joint caching and computing optimization problem with the goal to maximize the reliability of VR delivery. By analyzing the coupling caching and computing strategies at HMDs, sub-6 GHz and mmWave base stations (BSs), we further transform the problem into a multiple-choice multi-dimension knapsack problem. A best-first branch and bound algorithm and a difference of convex programming algorithm are proposed to obtain the optimal and sub-optimal solution, respectively. Numerical simulations demonstrate the performance improvement using the proposed algorithms, and reveal that caching more monocular videos at sub-6 GHz BSs and more stereoscopic videos at mmWave BSs can improve the VR delivery reliability efficiently.

关键词： Delays   Interference   Queueing analysis   Servers   Signal to noise ratio   Wireless communication   NOMA   Mobile edge computing   delay   Poisson cluster process   stochastic network calculus   orthogonal frequency division multiple access  

摘要： Wireless networks in next generation will provide users ubiquitous computing services with low delay by devices at the network edge, namely mobile edge computing (MEC). The intensive computation tasks can be partially offloaded to the MEC server via the wireless link and then processed through the MEC computation resources to cater for the delay demand. A parallel computation process is formed in the MEC network consists of local computation at MEC users (MUs) and MEC computation at MEC servers. However, the fluctuating wireless channel environment, changeable spatial distribution of MUs and the randomness of MEC servers' locations make it hard to characterize and guarantee the end-to-end quality of service requirements. In this work, we are devoted to analyze and optimize the overall delay bound for MEC networks under two orthogonal frequency division multiple access (OFDMA) strategies via stochastic network calculus (SNC). Specifically, Poisson cluster process is utilized to capture the randomness of MEC servers' and users' spatial locations and to derive the Laplace transform of interference suffered by an MU of interest. The upper bounds for the delay violation probability of two OFDMA strategies are established by exploiting SNC with the Mellin transform of signal-to-interference ratio. Furthermore, we propose an optimal task offloading scheme by minimizing the overall delay, which balances the local computation delay and MEC delay.

关键词： Mobile edge computing   computation offloading   latency   genetic algorithm   synchronous data flow graph  

摘要： With the rapid development of smart mobile devices, mobile applications are becoming more and more popular. Since mobile devices usually have constrained computing capacity, computation offloading to mobile edge computing (MEC) to achieve a lower latency is a promising paradigm. In this paper, we focus on the optimal offloading problem for streaming applications in MEC. We present solutions to find offloading policies of streaming applications to achieve an optimal latency. Streaming applications are modeled with synchronous data flow graphs. Two architecture assumptions are considered - with sufficient processors on both the local device and the MEC server, and with a limited number of processors on both sides. The problem is generally NP-complete. We present an exact algorithm and a heuristic algorithm for the former architecture assumption and a heuristic method for the latter. We carry out our experiments on a practical application and thousands of synthetic graphs to comprehensively evaluate our methods. The experimental results show that our methods are effective and computationally efficient.

关键词： Mobile edge computing  

摘要： Advancements in smart vehicle design have enabled the creation of Internet of Vehicle (IoV) technologies that can utilize the information provided by various sensors and wireless communication to perform complex functionality. Many of these functionalities rely on high computational power and low latency. Mobile Edge Computing (MEC) technologies have been proposed as a way to meet these requirements, as their proximity and decentralization provide unique benefits for networks like real-time communication, higher throughput, and flexibility. Diverse challenges to the process of offloading data in a MEC-enabled IoV network have emerged, such as offloading reliability in highly mobile environments, security for users within the same network, and energy management to keep users from being disincentivized to participate in the network. This article surveys research studies that use AI as part of the data offloading process, categorized based on four main issues: reliability, security, energy management, and service seller profit. Afterward, this article discusses challenges and future perspectives for IoV technologies. © 2022, CC BY-SA.

关键词： Mobile edge computing  

摘要： Aiming at the problem that fixed mobile edge computing (MEC) server is difficult to meet the needs of mobile users and temporary computing services, this study proposes a network resource allocation strategy based on unmanned aerial vehicle (UAV) cooperative edge computing. First, a UAV-aided MEC scene is designed, and a single UAV with an MEC server is used to provide auxiliary computing services for ground multiusers. Then, an optimization model aiming at total system delay is constructed by considering the system communication model and calculation model. Finally, Deep Q-Network is used to solve the optimization problem to obtain the best resource allocation scheme. Based on the experimental platform, the proposed strategy is demonstrated and analyzed. The results show that when the number of user equipment is 40, the total delay is about 33s, which is 35.29%, 31.25%, and 15.38% lower than other comparison strategies and effectively reduces the computing delay of users.

关键词： blockchain   logistics   mobile edge computing   security   unmanned aerial vehicle  

摘要： The "last mile" problem in logistics is challenging due to its low efficiency and high cost. To address this problem, Unmanned Aerial Vehicle (UAV) delivery such as drone delivery has been proposed and widely accepted as a promising solution. However, currently most of the existing UAV delivery systems are based on Cloud Computing which cannot efficiently meet the requirements of many real-time services in UAV delivery systems. Meanwhile, the security issues in UAV delivery systems also raise critical concerns due to the existence of multiple participants (such as the sender, middler, and receiver) who may not maintain a mutual trust relationship among them. How to secure the UAV delivery process in such an untrusted environment is still a challenging issue. In this paper, we propose a Mobile Edge Computing (MEC) and blockchain-based UAV delivery system to resolve the "last mile" problem in logistics. Specifically, based on the MEC architecture, the blockchain nodes are deployed on the edge nodes to facilitate and secure the UAV delivery process. To verify the effectiveness of our proposed solution, a MEC-based UAV delivery system prototype with a private blockchain on the Ethereum platform is implemented. Through the security analysis and performance evaluation, it is proven that our proposed solution can effectively solve the "last mile" problem and address the security issues in UAV delivery systems.